Multirate Minimum Variance Control Design and Control Performance Assessment: A Data-Driven Subspace Approach
Multirate Minimum Variance Control Design and Control Performance Assessment: A Data-Driven Subspace Approach
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DOI：
10.1109/tcst.2006.883240
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发表时间：
2007
影响因子：
4.8
通讯作者：
Xiaorui Wang;Biao Huang;Tongwen Chen
中科院分区：
文献类型：
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作者：
Xiaorui Wang;Biao Huang;Tongwen Chen
本文讨论了多采样率系统的最小方差控制（MVC）设计和基于MVC基准的控制性能评估。特别地，考虑了具有快控制更新速率和慢输出采样速率的双速率系统，这在实践中并不罕见。利用提升模型在状态空间框架下对多采样率系统进行分析，并利用提升技术推导出多采样率系统的子空间方程。从子空间方程出发，提出了多速率MVC准则和估计多速率MVC-基准方差或性能指标的算法。多速率最优控制器计算从一组输入/输出（I/O）开环实验数据，因此，这种方法是数据驱动的，因为它不涉及一个明确的模型。并行地，所提出的MVC基准估计算法需要一组开环实验数据和闭环常规操作数据。没有明确的模型，即，传递函数矩阵，马尔可夫参数，或相互作用矩阵，是必要的。这与传统的控制性能评估算法不同。最后通过一个仿真实例说明了所提出的方法
This paper discusses minimum variance control (MVC) design and control performance assessment based on the MVC-benchmark for multirate systems. In particular, a dual-rate system with a fast control updating rate and a slow output sampling rate is considered, which is not uncommon in practice. A lifted model is used to analyze the multirate system in a state-space framework and the lifting technique is applied to derive a subspace equation for multirate systems. From the subspace equation, the multirate MVC law and the algorithm are developed to estimate the multirate MVC-benchmark variance or performance index. The multirate optimal controller is calculated from a set of input/output (I/O) open-loop experimental data and, thus, this approach is data-driven since it does not involve an explicit model. In parallel, the presented MVC-benchmark estimation algorithm requires a set of open-loop experimental data and close-loop routine operating data. No explicit models, namely, transfer function matrices, Markov parameters, or interactor matrices, are needed. This is in contrast to traditional control performance assessment algorithms. The proposed methods are illustrated through a simulation example